! pip install networkx
! pip install plotly
! pip install colorlover
! pip install NRCLex
Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/ Requirement already satisfied: networkx in /usr/local/lib/python3.9/dist-packages (3.0) Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/ Requirement already satisfied: plotly in /usr/local/lib/python3.9/dist-packages (5.13.1) Requirement already satisfied: tenacity>=6.2.0 in /usr/local/lib/python3.9/dist-packages (from plotly) (8.2.2) Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/ Requirement already satisfied: colorlover in /usr/local/lib/python3.9/dist-packages (0.3.0) Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/ Requirement already satisfied: NRCLex in /usr/local/lib/python3.9/dist-packages (3.0.0) Requirement already satisfied: textblob in /usr/local/lib/python3.9/dist-packages (from NRCLex) (0.17.1) Requirement already satisfied: nltk>=3.1 in /usr/local/lib/python3.9/dist-packages (from textblob->NRCLex) (3.8.1) Requirement already satisfied: tqdm in /usr/local/lib/python3.9/dist-packages (from nltk>=3.1->textblob->NRCLex) (4.65.0) Requirement already satisfied: joblib in /usr/local/lib/python3.9/dist-packages (from nltk>=3.1->textblob->NRCLex) (1.1.1) Requirement already satisfied: regex>=2021.8.3 in /usr/local/lib/python3.9/dist-packages (from nltk>=3.1->textblob->NRCLex) (2022.10.31) Requirement already satisfied: click in /usr/local/lib/python3.9/dist-packages (from nltk>=3.1->textblob->NRCLex) (8.1.3)
import networkx as nx
import pandas as pd
from collections import Counter
import matplotlib.pyplot as plt
from plotly.offline import download_plotlyjs, init_notebook_mode, plot, iplot
from plotly.graph_objs import *
import plotly.graph_objects as go
init_notebook_mode(connected=True)
import nltk
nltk.download('stopwords')
from nltk.corpus import stopwords
from nltk import tokenize
import operator
from nrclex import NRCLex
import nltk
nltk.download('punkt')
import re
from collections import defaultdict
import colorlover as cl
from IPython.display import HTML
[nltk_data] Downloading package stopwords to /root/nltk_data... [nltk_data] Package stopwords is already up-to-date! [nltk_data] Downloading package punkt to /root/nltk_data... [nltk_data] Package punkt is already up-to-date!
df = pd.read_csv("corona_tweets.csv")
print("Num of rows:", df.shape[0])
Num of rows: 2550194
df['date']=pd.to_datetime(df['date'].str.slice(0,-5),format='%Y-%m-%dT%H:%M:%S')
df.head()
| date | user | tweet | |
|---|---|---|---|
| 0 | 2020-11-01 04:25:52 | bernadettecomm1 | RT @ClarkeMicah: Anti-lockdown Letters to MPs ... |
| 1 | 2020-11-01 04:25:52 | bluemeade7 | RT @petestrzok: RT is registered with the Depa... |
| 2 | 2020-11-01 04:25:52 | _raauull | RT @essmeraldasauce: COVID cases broke single-... |
| 3 | 2020-11-01 04:25:53 | purushothama_n | RT @SadhguruJV: By their very design, most cit... |
| 4 | 2020-11-01 04:25:53 | fato_frank | RT @RealCandaceO: Germany and France go back i... |
allTweets = df["tweet"].str.cat(sep=' ')
tweetWords = [word.strip(""" ,.:'\";""").lower() for word in allTweets.split()]
hashTags = [word for word in tweetWords if word.startswith("#")]
hashTagsCounter = Counter(hashTags)
hashTagsCounter.most_common(100)
[('#1', 87335),
('#covid19', 75713),
('#coronavirus', 21472),
('#covid', 13419),
('#lockdown', 11150),
('#covid_19', 8178),
('#covidiots', 6838),
('#lockdown2', 6576),
('#vote', 6328),
('#wewillnotcomply', 6109),
('#pandemic', 5692),
('#trump', 4568),
('#closetheschools', 3860),
('#wearamask', 3587),
('#marr', 3005),
('#breaking', 2942),
('#panicbuying', 2796),
('#closeschoolsnow', 2678),
('#…', 2587),
('#excludeduk', 2540),
('#nasaanangpangulo', 2532),
('#nhs', 2420),
('#art', 2340),
('#lockdownuk', 2282),
('#staysafe', 2199),
('#covid19uk', 2110),
('#halloween2020', 2096),
('#stayhome', 1888),
('#votehimout', 1884),
('#corona', 1859),
('#covid-19', 1824),
('#biden', 1811),
('#bidenharris2020', 1807),
('#wfh', 1786),
('#votebluetoendthenightmare', 1767),
('#halloween', 1717),
('#2ndlockdown', 1656),
('#florida', 1564),
('#election2020', 1478),
('#trumpdeliverednothing', 1408),
('#taiwanmodel', 1381),
('#trumpvirus', 1372),
('#', 1365),
('#workfromhome', 1356),
('#sarscov2', 1352),
('#withbidenwecan', 1335),
('#maga', 1317),
('#blood', 1253),
('#borisjohnson', 1171),
('#foxnews', 1163),
('#angamizacoronamachakos', 1157),
('#rapo', 1143),
('#socialdistancing', 1103),
('#auspol', 1082),
('#mentalhealth', 1053),
('#news', 1036),
('#uk', 1025),
('#usa', 990),
('#delhi', 988),
('#ridge', 924),
('#voteblue', 920),
('#happybirthdaysrk', 918),
('#endsars', 890),
('#health', 887),
('#indiafightscorona', 867),
('#coronavirusuk', 866),
('#trump2020', 843),
('#stayathome', 815),
('#blacklivesmatter', 813),
('#coronaviruspandemic', 812),
('#rollyph', 804),
('#bidenharris', 797),
('#smartnews', 795),
('#brexit', 756),
('#lockdownscostlives', 756),
('#bidenharristosaveamerica', 737),
('#maskup', 717),
('#sundaythoughts', 703),
('#sundaymorning', 702),
('#ai', 697),
('#quarantine', 696),
('#secondlockdown', 696),
('#lka', 688),
('#germany', 676),
('#srilanka', 675),
('#exclusive', 672),
('#happybirthdayshahrukhkhan', 670),
('#srk55', 664),
('#vaccine', 662),
('#votebidenharris2020', 662),
('#masks', 656),
('#electionday', 648),
('#c…', 642),
('#healthcare', 640),
('#joebiden', 638),
('#votebiden', 631),
('#covidー19', 629),
('#itcard', 626),
('#election202…', 611),
('#uklockdown2', 602)]
# make a new dataframe just with our hashtag
ukTag = df[df["tweet"].str.lower().str.contains("#lockdownuk", na=False, regex=False)].copy()
def addMentionedColumn(df):
def mentionsList(txt):
allWords = [word.strip(""" ,.:'\";""").lower() for word in txt.split()]
allNames = [word.strip("@") for word in allWords if word.startswith("@")]
uniqueNames = list(set(allNames))
return allNames
df["mentioned"] = df["tweet"].apply(mentionsList)
ukTag.iloc[1,2]
'No science to suggest a #SecondWave #FalsePositive results from inherently unreliable #COVID19 #PCRtest tests are being used to manufacture a "2nd wave" based on "new cases." https://t.co/5oOChVjRyU #MedicalTyranny #lockdownUK #Lockdown2 #ResistLockdown #Casedemic'
addMentionedColumn(ukTag)
ukTag.head()
| date | user | tweet | mentioned | |
|---|---|---|---|---|
| 284 | 2020-11-01 04:26:07 | actresscamilla | As we move into #Lockdown2 my Short Film SPIRA... | [] |
| 1336 | 2020-11-01 04:27:02 | myteaatom | No science to suggest a #SecondWave #FalsePosi... | [] |
| 5307 | 2020-11-01 04:30:16 | onlytruthtb | @BorisJohnson You are a worst person than comm... | [borisjohnson] |
| 5550 | 2020-11-01 04:30:28 | donnajaiel | RT @Cluckmuckcook: #lockdown #loaf ready . #lo... | [cluckmuckcook] |
| 12124 | 2020-11-01 04:35:56 | np_rn_kay | Anyone think some states will follow the UK’s ... | [] |
How many nodes and how many edges are in your mention graph?
ukTag_filtered = ukTag.loc[ukTag['mentioned'].apply(lambda x: len(x)>=1)]
ukTag_filtered.head()
| date | user | tweet | mentioned | |
|---|---|---|---|---|
| 5307 | 2020-11-01 04:30:16 | onlytruthtb | @BorisJohnson You are a worst person than comm... | [borisjohnson] |
| 5550 | 2020-11-01 04:30:28 | donnajaiel | RT @Cluckmuckcook: #lockdown #loaf ready . #lo... | [cluckmuckcook] |
| 22185 | 2020-11-01 04:44:18 | beinghuman_taj | RT @SattarFarooqui: #BorisJohnson\n#lockdownUK... | [sattarfarooqui] |
| 26246 | 2020-11-01 04:47:46 | jumprobert | RT @nazirafzal: Here’ the return of the COVID ... | [nazirafzal] |
| 40542 | 2020-11-01 05:00:24 | abbas_mahfooz | RT @SattarFarooqui: #BorisJohnson\n#lockdownUK... | [sattarfarooqui] |
ukTag_filtered.shape
(1116, 4)
ukTag.shape
(2300, 4)
# for all the tweets with your hashtag, build the mention graph
def mentionGraph(df):
g = nx.Graph()
for (index, date,user, tweet, mentionedUsers) in df.itertuples():
if user != '' and mentionedUsers != []:
for mentionedUser in mentionedUsers:
if mentionedUser != '':
if (user in g) and (mentionedUser in g[user]):
g[user][mentionedUser]["numberMentions"] += 1
else:
g.add_edge(user, mentionedUser, numberMentions=1)
return g
ukGraph_filtered = mentionGraph(ukTag_filtered)
print("# nodes:", len(ukGraph_filtered.nodes()))
print("# edges:", len(ukGraph_filtered.edges()))
# nodes: 1580 # edges: 1400
There are 1580 nodes and 1400 edges in the mention graph.
nx.draw(ukGraph_filtered, with_labels=False)
Build a histogram of the graph nodes’ degree (i.e., the degree distribution of the graph). What does the distribution in node degree tell you about how your network is structured?
degree_views = nx.degree(ukGraph_filtered)
degree_values = [degree for node, degree in degree_views]
deg_counter = Counter(degree_values)
degrees, counts = zip(*deg_counter.items())
# Create the histogram
plt.bar(degrees, counts)
plt.xlabel('Degree')
plt.ylabel('Frequency')
plt.title('histogram of graph nodes degree')
plt.show()
From the histogram, the distribution in node degree is right skew. Most of the degree falls below about 10, and there are some outliers.
Build a log-log scatterplot with the node degree (x-axis) and fraction of nodes with that degree (y-axis). Does your mention graph exhibit a power law trend? If not, can you explain why that may be?
node_total = len(ukGraph_filtered.nodes())
frac = {degree:count/node_total for degree, count in deg_counter.items()}
# Separate the keys (degrees) and values (fractions) into separate lists
degrees, fractions = zip(*frac.items())
# Create the log-log scatterplot
plt.scatter(degrees, fractions)
plt.xscale("log")
plt.yscale("log")
# Set axis labels
plt.xlabel("Node Degree")
plt.ylabel("Fraction of Nodes with Degree")
# Show the plot
plt.show()
In the mention graph, there exists power law trend. The power-law pattern has the form y = k * x^a. Here we use log for x and y axis, so the equation becomes: log(y) = log(k) + a*log(x).
The trend in our graph is the combination of two linear trend, which satisfies. As the log of node degree increase, the log of fraction first decrease, then remain horizontally after log(node degree) = 10.
For the highest-weighted edge, list the tweets (up to 25) for that edge and describe the interaction between these two users which explains why this edge has the highest weight. Note: edges are identified by the two nodes they connect, e.g., ⟨node1, node2⟩.
highest_edges = max(ukGraph_filtered.edges(data=True), key=lambda x: x[2]['numberMentions'])
highest_edges
('cardstreebeec', 'ebay_uk', {'numberMentions': 38})
count = 0
for (index, date, user, tweet, mentioned) in ukTag.itertuples():
for mentionedUser in mentioned:
if count <= 25:
if mentionedUser == 'cardstreebeec' and user == 'ebay_uk' or mentionedUser == 'ebay_uk' and user == 'cardstreebeec':
print('user:', user)
print('mentionedUser:', mentionedUser)
print('content of the tweet:', tweet)
count += 1
else:
break
user: cardstreebeec mentionedUser: ebay_uk content of the tweet: #BestOfBritish #Bestseller #bestselling Personalised Birthday Card UNICORN - Daughter Sister Granddaughter Niece Cousin https://t.co/isRwPtBPc9 via @eBay_UK #lockdown #lockdownUK #crafts user: cardstreebeec mentionedUser: ebay_uk content of the tweet: #BestOfBritish #lockdown #lockdownUK #Personalised VODKA Birthday Card -Male/Female Any Relation/18th 21st 30th 40th.. https://t.co/3SII72U1a8 via @eBay_UK user: cardstreebeec mentionedUser: ebay_uk content of the tweet: #lockdownUK #lockdown #handmade #homemade #UKCraftersHour #UKGiftHour Personalised Birthday Card Perfume Ladybird theme -Daughter Granddaughter Sister https://t.co/fcZajuzcV1 via @eBay_UK user: cardstreebeec mentionedUser: ebay_uk content of the tweet: #handmade #homemade #UKCraftersHour #UKGiftHour #lockdownUK #lockdown #waleslockdown #IrelandLockdown #Scotland #Christmas Handmade Personalised Disney Princesses Birthday Card Daughter Sister Niece etc https://t.co/3iQHTfFTqH via @eBay_UK user: cardstreebeec mentionedUser: ebay_uk content of the tweet: #handmade #homemade #UKCraftersHour #UKGiftHour #lockdownUK #lockdown #waleslockdown #IrelandLockdown #Scotland #Christmas Personalised Birthday Card Girl with Flowers Daughter Granddaughter Niece Sister https://t.co/SgNPFHSMIX via @eBay_UK user: cardstreebeec mentionedUser: ebay_uk content of the tweet: #handmade #homemade #UKCraftersHour #UKGiftHour #lockdownUK #lockdown #waleslockdown #IrelandLockdown #Scotland #Christmas Personalised Birthday Card - PERFUME BOTTLE -Daughter Sister Granddaughter Niece https://t.co/iSulg05CSy via @eBay_UK user: cardstreebeec mentionedUser: ebay_uk content of the tweet: #handmade #homemade #UKCraftersHour #UKGiftHour #lockdownUK #lockdown #waleslockdown #IrelandLockdown #Scotland #Christmas Personalised Birthday Card Cute Panda theme - Mum Daughter Granddaughter Niece https://t.co/7pVEprpHJH via @eBay_UK user: cardstreebeec mentionedUser: ebay_uk content of the tweet: #handmade #homemade #UKCraftersHour #UKGiftHour #lockdownUK #lockdown #waleslockdown #IrelandLockdown #Scotland #Christmas Personalised Birthday Card Star Wars Darth Vader theme https://t.co/F5LLkqQweA via @eBay_UK user: cardstreebeec mentionedUser: ebay_uk content of the tweet: #handmade #homemade #UKCraftersHour #UKGiftHour #lockdownUK #lockdown #waleslockdown #IrelandLockdown #Scotland #Christmas Personalised Birthday Card, CUPCAKES - Daughter, Sister, Granddaughter, Niece https://t.co/vbvbyeUQwB via @eBay_UK user: cardstreebeec mentionedUser: ebay_uk content of the tweet: #handmade #homemade #UKCraftersHour #UKGiftHour #lockdownUK #lockdown #waleslockdown #IrelandLockdown #Scotland #Christmas Personalised Birthday Card Garden Bird - Daughter Granddaughter Sister Niece https://t.co/ldxXium69v via @eBay_UK user: cardstreebeec mentionedUser: ebay_uk content of the tweet: #handmade #homemade #UKCraftersHour #UKGiftHour #lockdownUK #lockdown #waleslockdown #IrelandLockdown #Scotland #Christmas Personalised Birthday Card -Handbag Shoes &amp; Perfume - Sister,Granddaughter,Niece https://t.co/ydDrlrjlNv via @eBay_UK user: cardstreebeec mentionedUser: ebay_uk content of the tweet: #handmade #homemade #UKCraftersHour #UKGiftHour #lockdownUK #lockdown #waleslockdown #IrelandLockdown #Scotland #Christmas Personalised Birthday Card - Mum Daughter Granddaughter Niece - Glamorous MONKEY https://t.co/KUeTTO3HiL via @eBay_UK user: cardstreebeec mentionedUser: ebay_uk content of the tweet: #handmade #homemade #UKCraftersHour #UKGiftHour #lockdownUK #lockdown #waleslockdown #IrelandLockdown #Scotland #Christmas Personalised Birthday Card T-REX/TYRANOSAURUS DINOSAUR - Son, Grandson, Brother https://t.co/hpqimjdFBL via @eBay_UK user: cardstreebeec mentionedUser: ebay_uk content of the tweet: #handmade #homemade #UKCraftersHour #UKGiftHour #lockdownUK #lockdown #waleslockdown #IrelandLockdown #Scotland #Christmas Personalised Anniversary / Birthday Card -Any Name/Relation/Age Cocktail Drink https://t.co/KiYtKzTQdM via @eBay_UK user: cardstreebeec mentionedUser: ebay_uk content of the tweet: #handmade #homemade #UKCraftersHour #UKGiftHour #lockdownUK #lockdown #waleslockdown #IrelandLockdown #Scotland #Christmas Personalised Birthday Card Disney Aurora Any Name/Age Daughter Niece Sister etc https://t.co/XCSpiKKybn via @eBay_UK user: cardstreebeec mentionedUser: ebay_uk content of the tweet: #handmade #homemade #UKCraftersHour #UKGiftHour #lockdownUK #lockdown #waleslockdown #IrelandLockdown #Scotland #Christmas PERSONALISED FEMALE BIRTHDAY CARD &#039;GIRL in PARIS&#039; Daughter, Niece,Sister,Auntie https://t.co/zIMFvzRAEi via @eBay_UK user: cardstreebeec mentionedUser: ebay_uk content of the tweet: #handmade #homemade #UKCraftersHour #UKGiftHour #lockdownUK #lockdown #waleslockdown #IrelandLockdown #Scotland #Christmas PERSONALISED HAPPY BIRTHDAY CARD HANDBAG THEME DAUGHTER, GRANDDAUGHTER ,SISTER https://t.co/kWTVdu9x9B via @eBay_UK user: cardstreebeec mentionedUser: ebay_uk content of the tweet: #handmade #homemade #UKCraftersHour #UKGiftHour #lockdownUK #lockdown #waleslockdown #IrelandLockdown #Scotland #Christmas PERSONALISED BIRTHDAY CARD &#039;Girl at the Mirror&#039; DAUGHTER, SISTER, NIECE etc https://t.co/173TcGqDOk via @eBay_UK user: cardstreebeec mentionedUser: ebay_uk content of the tweet: #handmade #homemade #UKCraftersHour #UKGiftHour #lockdownUK #lockdown #waleslockdown #IrelandLockdown #Scotland #Christmas HANDMADE PERSONALISED cute birthday card 18th 21st 30th 40th DAUGHTER SISTER https://t.co/ADp6s3530t via @eBay_UK user: cardstreebeec mentionedUser: ebay_uk content of the tweet: #handmade #homemade #UKCraftersHour #UKGiftHour #lockdownUK #lockdown #waleslockdown #IrelandLockdown #Scotland #Christmas Flower Box PERSONALISED Birthday Greeting Card ANY NAME Sister, Auntie, Mum etc https://t.co/vGB4zRTr1U via @eBay_UK user: cardstreebeec mentionedUser: ebay_uk content of the tweet: #handmade #homemade #UKCraftersHour #UKGiftHour #lockdownUK #lockdown #waleslockdown #IrelandLockdown #Scotland #Christmas PERSONALISED GUITAR BIRTHDAY CARD Any Name, Son, Grandson, Brother, Nephew etc https://t.co/wfdF47i7QX via @eBay_UK user: cardstreebeec mentionedUser: ebay_uk content of the tweet: #handmade #homemade #UKCraftersHour #UKGiftHour #lockdownUK #lockdown #waleslockdown #IrelandLockdown #Scotland #Christmas PERSONALISED FEMALE BIRTHDAY CARD &#039;GIRL in PARIS&#039; Daughter, Niece,Sister,Auntie https://t.co/zIMFvzRAEi via @eBay_UK user: cardstreebeec mentionedUser: ebay_uk content of the tweet: #handmade #homemade #UKCraftersHour #UKGiftHour #lockdownUK #lockdown #waleslockdown #IrelandLockdown #Scotland #Christmas PERSONALISED Birthday Card ANY NAME Friend Sister Mum Auntie... HIGH HEELED SHOE https://t.co/4JfAzVOtei via @eBay_UK user: cardstreebeec mentionedUser: ebay_uk content of the tweet: #handmade #homemade #UKCraftersHour #UKGiftHour #lockdownUK #lockdown #waleslockdown #IrelandLockdown #Scotland #Christmas PERSONALISED Birthday Cake &amp; Balloons Birthday Card - Any Name / Any Age https://t.co/WoHb1oELuy via @eBay_UK user: cardstreebeec mentionedUser: ebay_uk content of the tweet: #handmade #homemade #UKCraftersHour #UKGiftHour #lockdownUK #lockdown #waleslockdown #IrelandLockdown #Scotland #Christmas PERSONALISED 18th 21st 30th 40th Birthday Card Any Name/Age - High Heeled Shoe https://t.co/K6eaKsUMOq via @eBay_UK user: cardstreebeec mentionedUser: ebay_uk content of the tweet: #handmade #homemade #UKCraftersHour #UKGiftHour #lockdownUK #lockdown #waleslockdown #IrelandLockdown #Scotland #Christmas HANDMADE PERSONALISED CHRISTMAS ART DECO CARD MUM ,DAD ,SON,DAUGHTER https://t.co/nTsmnDj0yQ via @eBay_UK
From the output above, among all of these edges, user @ebay_uk were mentioned by usercardstreebeec. In twitter, user @ebay_uk is an official account for large e-commerce business group; user @cardstreebeec is an official account for gift card solder and that solder holds a business account for gift card selling on Ebay (UK). The user mentioned @ebay_uk to let customers know their platform for gift card selling.
Ebay is one of the largest e-commerce business group in the world, and has a large number of followers, mentioning that account could lead to a high weight on the corresponding edge.
Moreover, due to covid situation, the e-commerce business developed fast and the user @cardstreebeec uses hastag like lockdown and lockdownuk, to attract potential customers who cannot celebrate birthday/ anniversary with their family due to pandamic.
Provide a visualization of the mention graph in which the edges visually (e.g., color, weight, etc.) reflects its weight (i.e., the number of mentions). Note: Be sure to use layouts to help make the visualizations clear.
def configure_plotly_browser_state():
import IPython
display(IPython.core.display.HTML('''
<script src="/static/components/requirejs/require.js"></script>
<script>
requirejs.config({
paths: {
base: '/static/base',
plotly: 'https://cdn.plot.ly/plotly-latest.min.js?noext',
},
});
</script>
'''))
def plotNetworkSize(graph):
scatters=[]
# make all the edges
for (node1, node2) in graph.edges():
x0, y0 = graph.nodes[node1]['pos']
x1, y1 = graph.nodes[node2]['pos']
edgeWidth = graph[node1][node2]['numberMentions']
s = Scatter(
x=[x0, x1],
y=[y0, y1],
hoverinfo='none',
mode='lines',
line=scatter.Line(width=edgeWidth ,color='#888'))
scatters.append(s)
# make all the nodes
for node in graph.nodes():
xPos, yPos = graph.nodes[node]['pos']
s = Scatter(
x=[xPos],
y=[yPos],
hoverinfo='none',
mode='markers',
marker=dict(
color="#888",
size=nx.degree(graph,node)*2,
line=dict(width=2)))
scatters.append(s)
layout = Layout(showlegend=False)
fig = Figure(data=scatters, layout=layout)
iplot(fig, show_link=False)
def applyLayout(graph, layoutFunc):
posDict = layoutFunc(graph)
nx.set_node_attributes(graph, name="pos", values=posDict)
ukGraph_filtered_mention = ukGraph_filtered.copy()
applyLayout(ukGraph_filtered_mention, nx.spring_layout)
configure_plotly_browser_state()
plotNetworkSize(ukGraph_filtered_mention)
ukTag['tweet'][:10]
284 As we move into #Lockdown2 my Short Film SPIRA... 1336 No science to suggest a #SecondWave #FalsePosi... 5307 @BorisJohnson You are a worst person than comm... 5550 RT @Cluckmuckcook: #lockdown #loaf ready . #lo... 12124 Anyone think some states will follow the UK’s ... 22185 RT @SattarFarooqui: #BorisJohnson\n#lockdownUK... 23316 'Time for further measures': UK PM imposes sec... 24484 Here we go again on our own.....#COVIDIOTS #lo... 26246 RT @nazirafzal: Here’ the return of the COVID ... 28039 James Bond - #SirSeanConnery is no more, Four-... Name: tweet, dtype: object
def get_top_k (df, k, value_column='tweet'):
stop = set(stopwords.words('english'))
stop.add('https')
stop.add('rt')
stop.add('co')
counter = Counter()
for twt in df[value_column]:
counter.update([word.lower()
for word
in tokenize.regexp.regexp_tokenize(twt, pattern='\w+')
if word.lower() not in stop and word.isalpha() and len(word)>=2])
topk = counter.most_common(k)
return topk
top10 = get_top_k(ukTag, 10)
top10
[('lockdownuk', 2303),
('lockdown', 1538),
('covid', 240),
('go', 208),
('borisjohnson', 203),
('schools', 189),
('people', 184),
('boris', 163),
('new', 160),
('amp', 152)]
The hastag chose was #lockdownuk, so the most frequent non-stopwords are #lockdownuk, in order to understand the theme better, we get top 10 most frequent non-stopwords, we get borisjohnson, covid, people and boris, the theme is about people 's comments for UK's covid policy and for British Prime Minister Boris Johnson.
In a visualization of the mention graph add hover information for the nodes which lists the 3 most common words for each user. Indicate the nodes that have no words visually. For those nodes that have words, are the words similar for connected users or are the words different? If not the same, do the words have similar themes?
def get_topk_user(df, kwords, user, value_column='tweet'):
stop = set(stopwords.words('english'))
counter = Counter()
user_df = df[df["user"] == user]
reviews = user_df['tweet'].values
for review in reviews:
counter.update([word.lower()
for word
in re.findall(r'\w+', review)
if word.lower() not in stop and len(word) > 2])
topk = counter.most_common(kwords)
topk = [n for n, d in topk]
return topk
# map purd color scale to 300 cells
purd = cl.scales['9']['seq']['PuRd']
purd300 = cl.interp(purd, 300)
HTML(cl.to_html(purd300))
def plotNetworkSizeColor_addhover(graph):
closenessCentr = nx.closeness_centrality(graph)
maxCentr = max(closenessCentr.values())
minCentr = min(closenessCentr.values())
scatters=[]
for (node1, node2) in graph.edges():
x0, y0 = graph.nodes[node1]['pos']
x1, y1 = graph.nodes[node2]['pos']
edgeWidth = graph[node1][node2]['numberMentions']
s = Scatter(
x=[x0, x1],
y=[y0, y1],
hoverinfo='none',
mode='lines',
line=scatter.Line(width=edgeWidth ,color='#888'))
scatters.append(s)
for node in graph.nodes():
nodeCentr = closenessCentr[node]
nodeColor = int(299*(nodeCentr-minCentr)/(maxCentr-minCentr))
xPos, yPos = graph.nodes[node]['pos']
top_3_words = get_topk_user(df, 3, node)
if len(top_3_words) > 0:
hover_text = "User: %s <br>" % (node) + "<br> Top 3 words: %s" % ', '.join(top_3_words)
else:
hover_text = "User: %s <br>" % (node) + "<br> No top words found"
s = Scatter(
x=[xPos],
y=[yPos],
text=hover_text,
hoverinfo='text',
mode='markers',
marker=dict(
color=["#ffddff" if len(top_3_words) == 0 else purd300[nodeColor]],
size=nx.degree(graph,node)*2,
line=dict(width=2)))
scatters.append(s)
layout = Layout(showlegend=False)
fig = Figure(data=scatters, layout=layout)
iplot(fig, show_link=False)
ukGraph_filtered_top3 = ukGraph_filtered.copy()
applyLayout(ukGraph_filtered_top3, nx.spring_layout)
configure_plotly_browser_state()
plotNetworkSizeColor_addhover(ukGraph_filtered_top3)
Note: in the graph above, nodes with no words are color in pink (##ffddff)
For those nodes that have words, the top 3 most common words for most of the connected users are similar (in related theme). Some connected users' top 3 most common words are same. For example, user @lez_the and user @midworldmo have the same top 3 most common words (fokawolf, new, rules).
Using PageRank and a 2nd centrality measure of your choosing, calculate the centrality of the nodes on your graph based on each of the measures. Provide a mention graph visualization for each measure that demonstrates the centrality value of each node using a visual property (size, color, etc) for each of the centrality measures.
Here I choose betweenness centrality
#pagerank
page_centr = nx.pagerank(ukGraph_filtered)
page_centr
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'draxgolfclub': 0.00034559087028249104,
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'richardosman': 0.00048725584994014856,
'xanderarmstrong': 0.00048725584994014856,
'ammanahmeddyc': 0.0003458053046794939,
'its_wordell': 0.0006329113924050633,
'nadinedorries': 0.0006329113924050633,
'justmekz_': 0.0003444317242688068,
'celtjules66': 0.00036246599767501527,
'nhsstaffcovid19': 0.00036246599767501527,
'pdxraj': 0.00044755442905198195,
'valsteed62': 0.0003917472385696556,
'walstowza': 0.00034359439081313903,
'stuarcher': 0.0006329113924050633,
'mikeparry8': 0.0006329113924050633,
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'dairy_is_scary': 0.0003434440845515584,
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'marc_reading': 0.001215533562264722,
'ttgtravelqueen': 0.00043870400245184357,
'degsy_dj': 0.00043870400245184357,
'flynn_debbie': 0.00043870400245184357,
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'dhaman': 0.00044755442905198195,
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'tiredumar': 0.00048725584994014856,
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'the_iod': 0.0004144280787076912,
'cbitweets': 0.0004144280787076912,
'bw_businesswest': 0.0004144280787076912,
'sardinesmag': 0.0008394372523171014,
'uksciencechief': 0.00033276743643001515,
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'diy_southampton': 0.0006329113924050633,
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'timothygmitch': 0.0006329113924050633,
...}
def plotNetworkSizeColor_centrality(graph, centrality_data):
maxCentr = max(centrality_data.values())
minCentr = min(centrality_data.values())
scatters=[]
for (node1, node2) in graph.edges():
x0, y0 = graph.nodes[node1]['pos']
x1, y1 = graph.nodes[node2]['pos']
edgeWidth = graph[node1][node2]['numberMentions']
s = Scatter(
x=[x0, x1],
y=[y0, y1],
hoverinfo='none',
mode='lines',
line=scatter.Line(width=edgeWidth ,color='#888'))
scatters.append(s)
mydict = centrality_data
for node in graph.nodes():
nodeCentr = centrality_data[node]
nodeColor = int(299*(nodeCentr-minCentr)/(maxCentr-minCentr))
xPos, yPos = graph.nodes[node]['pos']
if node in mydict:
centrality = mydict[node]
hover_text = "User: %s <br>" % (node) + "<br> centrality %s" % (centrality)
s = Scatter(
x=[xPos],
y=[yPos],
text=hover_text,
hoverinfo='text',
mode='markers',
marker=dict(
color=purd300[nodeColor],
size=nx.degree(graph,node)*2,
line=dict(width=2)))
scatters.append(s)
layout = Layout(showlegend=False)
fig = Figure(data=scatters, layout=layout)
iplot(fig, show_link=False)
ukGraph_filtered_centrality_pr = ukGraph_filtered.copy()
applyLayout(ukGraph_filtered_centrality_pr, nx.spring_layout)
configure_plotly_browser_state()
plotNetworkSizeColor_centrality(ukGraph_filtered_centrality_pr, page_centr)
#betweenness
between_centr = nx.betweenness_centrality(ukGraph_filtered)
between_centr
{'onlytruthtb': 0.0,
'borisjohnson': 0.03540708771837453,
'donnajaiel': 0.0,
'cluckmuckcook': 0.0,
'beinghuman_taj': 0.0,
'sattarfarooqui': 8.026770886259854e-07,
'jumprobert': 0.0,
'nazirafzal': 0.00034916453355230364,
'abbas_mahfooz': 0.0,
'hiteshp75014563': 0.0,
'zeenewsenglish': 0.0008387975576141547,
'stefing': 2.000518282421716e-05,
'markstokesouth': 3.130440645641343e-05,
'toryfibs': 0.0036614115397674324,
'prabpkumar': 2.000518282421716e-05,
'ziadmiqdadi': 0.0,
'_emmanuel_y': 0.0033287018865319614,
'ianm_ah': 0.00033767318738573896,
'keir_starmer': 0.002236035199722875,
'sriramansri': 2.000518282421716e-05,
'aambhartiya30': 0.0,
'this_worldsucks': 2.000518282421716e-05,
'prasadreddyshvd': 0.0,
'dilbag_koundal': 0.0,
'rajapatel878': 0.0,
'tla5215596': 0.0,
'india194715aug': 0.0,
'bbcsarika': 2.247495848152759e-05,
'mohith__rai': 0.0008572591306525523,
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'thedailypioneer': 0.0,
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'_autocrat': 0.0,
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'deaneuesden': 8.026770886259853e-06,
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'pmcf12': 0.0,
'redhen90': 8.026770886259854e-07,
'kingban50512521': 0.0,
'dailymirror': 8.026770886259854e-07,
'10newsfirst': 0.0,
'phoebebowden': 0.0,
'sejalbhagat54': 2.000518282421716e-05,
'fenil866': 0.0,
'pennyware18': 0.0,
'solon594bce': 0.0,
'nadiq38': 0.0,
'modupeofficial': 0.0,
'giovagando984': 0.0,
'manishkol29': 0.0,
'bhuvinkothari': 0.0,
'maddie_tiny': 2.000518282421716e-05,
'leahy7leahy': 0.0,
'sms_venkat': 0.0,
'trendsest20': 0.0,
'axispink': 2.000518282421716e-05,
'ashepherdson2': 0.0,
'theladyfiction': 0.0,
'jellliott': 0.0,
'imsaleemalikhan': 0.0,
'molxuu': 0.0,
'stacksproperty': 0.0,
'propertyjourn': 0.0,
'saharbilal__': 2.000518282421716e-05,
'tebbsey': 0.0,
'1judilove': 4.4147239874429194e-05,
'kaywhise': 0.0,
'reannetamia': 0.0,
'shineonsilver': 0.0,
'lucyfrancis_tri': 2.4080312658779563e-06,
'mikezafc_': 0.0,
'jahangi89148935': 0.0,
'vldnn_': 0.0,
'its_lysette': 0.0,
'nazraosman': 0.0,
'realkarlroe': 1.6053541772519708e-07,
'syndicate': 4.013385443129927e-06,
'lifeoftom…': 4.013385443129927e-06,
'andream31914639': 0.0,
'jerryhicksunite': 0.0,
'wiqarsayed': 0.0,
'gunnayssl': 0.0,
'caroloptera': 8.026770886259854e-07,
'blazetv': 0.0,
'blazetvuk': 0.0,
'goldie_77': 0.0,
'ianheadley': 0.0,
'davidlsmith1976': 0.0,
'mandynewman12': 0.0,
'jorichardskent': 4.816062531755913e-06,
'coppinsstephen': 0.0,
'susannareid100': 0.0,
'vegpedaller': 0.0,
'realmattlucas': 0.0,
'manthedarman': 0.0,
'northern_travel': 0.0,
'loveisbrilliant': 0.009741354518331,
'socialistgeek': 0.0,
'alanmoore4816': 0.0,
'smorris48073327': 0.0,
'_jayldn': 0.0,
'nrissaj': 0.0,
'socaholict': 0.0,
'ianace0': 0.0,
'thetodaysgolfer': 0.0021094353889090896,
'1yb___': 0.0,
'amateurgolfer': 0.0,
'probreadbutter': 0.0,
'liambamforth': 0.0,
'jhalakportal': 2.4080312658779563e-06,
'iamtridha': 0.0,
'teamtridha': 0.0,
'tridhachoudhury': 0.0,
'a_kar13': 0.0019324450908670598,
'relovedreams': 0.0,
'lasinclairio': 0.0,
'robertajane_u': 0.0,
'calmcarm': 0.0,
'rkazandjian': 0.0,
'_kharisss': 0.0,
'plumber2thestar': 0.0,
'spaceangel1964': 0.0,
'tommousk': 0.0,
'garyrumbell': 0.0,
'djaysteve': 0.0,
'piersmorgan': 0.003901542364059763,
'christchurchn14': 0.0008066904740691153,
'churchofengland': 0.0010691658820498125,
'bpedmonton': 0.0,
'bishopsarahm': 0.0,
'southgatechoir': 0.0,
'danstonee': 0.0,
'cllrcarolinek': 0.0,
'jonathan1922': 0.0,
'adam_james2206': 0.0,
'niyah55553771': 0.0,
'amberzaidi20': 0.0,
'arynewsofficial': 8.026770886259853e-06,
'isabellatofts': 0.0,
'toucheadrian': 0.004245417802302725,
'emmanuelmacron': 0.0,
'maajidnawaz': 0.0,
'sayeedawarsi': 0.0,
'davidlammy': 0.0008042824428032374,
'lozzafox': 0.0,
'jkcorden': 0.0,
'garylineker': 0.0,
'skynewsbreak': 0.0,
'kayburley': 0.0,
'martindaubney': 0.001468122948069174,
'prisonplanet': 0.0,
'mashable': 0.0,
'_mrproductions': 0.0,
'n_fromthe9': 0.0,
'rozaeliaa': 0.0,
'gayedalton': 0.0,
'truesinews': 0.0002696995017783311,
'matthancock': 0.002885317097386519,
'fairwayfromhome': 0.0,
'draxgolfclub': 0.0,
'lordofthevan': 0.0,
'andrew_adonis': 0.0,
'dawnneesom': 0.0015161947907967883,
'adilray': 0.0,
'liamridgill': 0.0,
'skiptothelu2': 0.0,
'villa_ds': 0.0,
'roofingthorley': 0.0,
'unclebobw': 0.0019324450908670598,
'thatmrdan': 0.0,
'charlick75': 0.0,
'allanholloway': 0.0,
'jedimasteriman': 0.0,
'jharvhelp': 0.0002696995017783311,
'devisridhar': 0.0,
'bbcnews': 0.003569713641972155,
'dannypgolf89': 0.0,
'absurdiajohn': 2.4080312658779563e-06,
'indiebookshopuk': 0.0,
'theclique_uk': 0.0,
'bookbloggershub': 0.0,
'jamesgolf07': 0.0,
'plx111': 0.0,
'yagga26evans': 0.0,
'cinthya_fer': 0.0,
'emmaison_gi': 0.0,
'gulf_intel': 8.026770886259853e-06,
'naraic_': 1.6053541772519708e-07,
'neilbellamy4': 0.0,
'muzz500photo': 2.4080312658779563e-06,
'henrysmithuk': 0.0,
'gatwick_airport': 0.0,
'heathrowairport': 0.0,
'revengebunny': 0.0,
'akajames101': 0.0018885503118249293,
'fmwales': 0.0,
'procartoonists': 0.0,
'mikestokoe': 8.026770886259854e-07,
'dhmck': 0.0,
'davemckflit': 0.0,
'smallbizshoutuk': 0.0,
'mazieandmorris': 4.816062531755913e-06,
'ossien1': 0.0,
'freelife118': 0.0,
'ivy_middletonuk': 0.00505847101252096,
'jamie_saris': 0.0,
'johnredwood': 0.0,
'tejsingh84': 0.0,
'rinaxxa': 0.0,
'robrobbedwards': 0.0,
'croslandheathgc': 0.0,
'mkhristina': 0.0,
'uncaddie': 0.0,
'stomkinson1': 0.0,
'catchcarter_now': 0.0,
'sliktrik': 0.0,
'lily_piaf': 0.0,
'yellowcolonia': 0.0,
'cm_forster': 0.0,
'oldyottie': 0.0,
'rathbonekim': 0.0,
'abdi_segulle': 0.00033767318738573896,
'jnlee': 0.0,
'mousetoney': 0.0,
'southlondonfilm': 0.0,
'georgioufilms': 0.0,
'_rebeccasmith': 0.0,
'econews2021': 0.0,
'jeanstaffordba1': 0.0,
'pughie87': 0.0,
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'damsapril': 0.0,
'danny_willo': 0.0,
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'tppbo': 0.0,
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'm1chellemejia': 0.0,
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'karenwrighton': 0.0013212231226190098,
'conservatives': 0.002823063358919162,
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'sharghiara': 0.0,
'ipeclub': 0.0,
'shortnblunttho': 0.0,
'christinasosseh': 2.000518282421716e-05,
'danfroggy1990': 0.0,
'animal_leaks': 0.0,
'jackhan32443566': 8.026770886259854e-07,
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'skyeforcex': 0.0,
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'hayley_core_v': 0.0,
'newsfrommichiel': 0.0,
'michaelgove': 0.003444623372729593,
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'_grvmishra': 0.0,
'otvnews': 8.026770886259854e-07,
'neverlandsturom': 7.224093797633868e-06,
'theaachillea': 0.0,
'thismorning': 7.224093797633868e-06,
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'venici': 2.4080312658779563e-06,
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'flynn_debbie': 0.0,
'ralzahrani2020': 0.0,
'jameerahamad10': 0.0,
'williamsjon': 0.0,
'dhaman': 0.0,
'veganroo': 0.0,
'mypvassistant': 0.0,
'afriwoman': 0.0002696995017783311,
'guardian?': 0.0,
'islander_sarah': 0.0,
'harryvederci11': 0.0,
'harshadsharma23': 0.0,
'mo_americanoid': 0.0,
'charlesinrome': 0.0,
'crux': 0.0,
'leannespurs': 0.0,
'tiredumar': 0.0,
'oli_bleader': 4.816062531755913e-06,
'bleadernews': 0.0,
'the_iod': 0.0,
'cbitweets': 0.0,
'bw_businesswest': 0.0,
'sardinesmag': 0.0008058877969804894,
'uksciencechief': 0.0,
'cmo_england': 0.0002696995017783311,
'allenow83890817': 0.0,
'nc_gamingclub': 1.6053541772519708e-07,
'diy_southampton': 0.0,
'kristiannedrake': 0.0,
'laraoyedele': 0.0,
'hannahfearn': 0.0,
'mightysprite1': 0.0,
'oflynnsocial': 0.0,
'gingerannie34': 8.026770886259854e-07,
'edmontongreen1': 0.0,
'enfieldcouncil': 0.0,
'dharford79': 0.0,
'hubofhopeuk': 0.0,
'ukgastrodr': 0.0,
'boxermk2': 8.026770886259854e-07,
'jonjonessnr': 0.0,
'clairemlodge': 0.0,
'akondakor': 0.0,
'alfiesykess': 0.0,
'neha_bandi': 0.0,
'selcobw': 0.0,
'misskaybiology': 0.0,
'i_nautilus': 2.247495848152759e-05,
'mittens57': 0.0,
'ajwshaughnessy': 0.0,
'vsmacdonald': 0.0,
'stopfaanextgen': 0.0,
'nataliesanford': 0.0,
'nmcnews': 0.0,
'colinslondoncab': 0.0,
'tazobrieeen': 0.0,
'ldndan': 0.0,
'plymouthcc': 0.0,
'sevadelsur': 0.0,
'nsholmes1': 4.816062531755913e-06,
'micahrichards': 0.0,
'bigpaparunner': 0.0,
'henry_the_brave': 0.0,
'islasfight': 0.0,
'charlpannell': 0.0,
'jonathon793793': 0.0,
'loudmouthkid62': 2.4080312658779563e-06,
'marklandler': 0.0,
'nikikitsantonis': 0.0,
'nytimes': 0.0,
'bigbloc11726950': 0.0,
'andyb_ward': 8.026770886259854e-07,
'deborahmeaden': 0.0,
'celestinogutirr': 0.0,
'aprilinparis49': 0.0,
'drdaver1': 0.0,
'ichrismahoney': 0.0,
'kris56725154': 0.0,
'peachyttea': 0.0,
'rupinder_hardy': 1.043480215213781e-05,
'noisymv': 0.0,
'ottersun1': 0.0005489054346804716,
'socialm85897394': 0.0,
'pritipatel': 0.0005654079785272693,
'houseofcommons': 0.0,
'iamadamevans': 8.026770886259854e-07,
'michaelgove?': 0.0,
'sophyridgesky': 0.0,
'bezzer1984': 0.0,
'kathym2016': 0.0,
'talkradio': 0.0011655153854216165,
'gilljames54': 0.0,
'serotiny23': 0.0002696995017783311,
'philgregson': 0.0,
'gears': 0.0,
'k3vw35t': 0.0,
'pointlessbrexit': 0.0,
'ianrohanmd': 0.0,
'rohantime': 0.0,
'poems_by_tessy': 0.0,
'runwithmartin': 8.026770886259854e-07,
'secretgrassroo3': 4.816062531755913e-06,
'coacha95': 0.0,
'secretgrassroo2': 0.0,
'burnskempson': 0.0,
'miftyjackson': 0.0,
'kdbsuff': 0.0,
'ejs_12': 0.0,
'simpson_liz': 0.0,
'msbatoyou': 0.0002696995017783311,
'scie_socialcare': 0.0,
'strassbergfc': 0.0,
'dbricknell10': 0.0,
'michael_liddle9': 0.0,
'susan_smith2405': 2.4080312658779563e-06,
'studiosociety': 0.0,
'chopchopldn': 0.0,
'westfieldlondon': 0.0,
'adamtlambertt': 0.0,
'author_f_foxx': 0.0,
'aaronr1988': 0.0,
'ali79596850': 2.247495848152759e-05,
'antiwokebritain': 0.0,
'rebeccalgray_': 0.0,
'ladyroheryn': 0.0,
'joods': 0.0,
'thefreds': 0.0,
'jas_cdx': 0.0,
'darnevakaras': 0.0,
'vivyouell': 0.0005385963264680362,
'westreservoir': 0.0,
'hackneyabbott': 0.0,
'politicssilver': 1.6053541772519707e-06,
'thatguycorey20': 0.0,
"borisjohnson's": 1.6053541772519707e-06,
'roni56248402': 0.0,
'timothygmitch': 0.0,
...}
ukGraph_filtered_centrality_bc = ukGraph_filtered.copy()
applyLayout(ukGraph_filtered_centrality_bc, nx.spring_layout)
configure_plotly_browser_state()
plotNetworkSizeColor_centrality(ukGraph_filtered_centrality_bc, between_centr)
(1) List the 5 most central nodes for each of the centrality measures. Discuss the following:
sorted_between_centr = sorted(between_centr.items(), key=lambda x:x[1], reverse = True)
sorted_between_centr[:5]
[('borisjohnson', 0.03540708771837453),
('julianstorey', 0.009992398847089703),
('loveisbrilliant', 0.009741354518331),
('mytweetuk', 0.006815724987977669),
('jamesdelingpole', 0.005088972741888747)]
sorted_page_centr = sorted(page_centr.items(), key=lambda x:x[1], reverse = True)
sorted_page_centr[:5]
[('ivy_middletonuk', 0.027290932328923163),
('thetodaysgolfer', 0.02160730950735282),
('_emmanuel_y', 0.017381794660605892),
('borisjohnson', 0.01464317666705259),
('nazirafzal', 0.009080932855370113)]
The top 5 most central nodes listed using both betweenness centrality and pagerank includes @borisjohnson, but the nodes in pagerank have more followers in twitter than nodes in betweenness centrality.
Reason: Pagerank is more suitable for large network and highly asymmetric link patterns (e.g. social network where some users have many more followers than others). Pagerank takes into account the quality of income links, not just the number of income links. However, betweenness centrality only considers the number of shortest paths that pass through a node.
(2) What centrality measure is more useful for your network? Why?
Pagerank is more useful for my network. The tag I chose is #lockdownuk, which is about policy and politics in UK during COVID-19. PageRank considers the entire network structure when calculating centrality, which could provide a more accurate measure of overall influence and importance in the network. In contrast, betweenness centrality only considers the local structure around each node. The influence of politics is countrywide and worldwide, using pagerank we could know how other authoritative persons think about the politics, which is more reasonable and meaningful.
Using NRCLex, evaluate each tweet and determine the number of words in each dimension of emotion. Construct a chart showing the number of words in each emotion. Which emotion is most frequent in the data? Does that make sense in the context of your chosen hash-tag? Explain why or why not by providing example tweets to support your claims
ukTag_filtered
| date | user | tweet | mentioned | |
|---|---|---|---|---|
| 5307 | 2020-11-01 04:30:16 | onlytruthtb | @BorisJohnson You are a worst person than comm... | [borisjohnson] |
| 5550 | 2020-11-01 04:30:28 | donnajaiel | RT @Cluckmuckcook: #lockdown #loaf ready . #lo... | [cluckmuckcook] |
| 22185 | 2020-11-01 04:44:18 | beinghuman_taj | RT @SattarFarooqui: #BorisJohnson\n#lockdownUK... | [sattarfarooqui] |
| 26246 | 2020-11-01 04:47:46 | jumprobert | RT @nazirafzal: Here’ the return of the COVID ... | [nazirafzal] |
| 40542 | 2020-11-01 05:00:24 | abbas_mahfooz | RT @SattarFarooqui: #BorisJohnson\n#lockdownUK... | [sattarfarooqui] |
| ... | ... | ... | ... | ... |
| 2381978 | 2020-11-02 02:36:34 | sarah76723558 | RT @azardsphere: Get it into your heads fast B... | [azardsphere] |
| 2405169 | 2020-11-02 02:51:30 | gardenswhatever | RT @Ncknwmn: The week in cartoons in @thesund... | [ncknwmn] |
| 2518010 | 2020-11-02 04:02:04 | joe_dee01 | RT @BenToni17: Lockdown Idiocracy perfectly de... | [bentoni17] |
| 2539444 | 2020-11-02 04:17:32 | legendtaleskin1 | https://t.co/g5crSkI5Pw… via @amazon Perfect w... | [amazon] |
| 2549201 | 2020-11-02 04:24:29 | dunlin57 | RT @Sideshow_Matt: This aged well, didn't it?\... | [sideshow_matt] |
1116 rows × 4 columns
emotions_dict = {}
for tweet in ukTag_filtered["tweet"]:
text_object = NRCLex(tweet)
num_words = text_object.raw_emotion_scores
for emo in num_words:
if emo in emotions_dict:
emotions_dict[emo] = emotions_dict[emo] + num_words[emo]
else:
emotions_dict[emo] = num_words[emo]
emotions_dict
{'negative': 463,
'positive': 680,
'trust': 349,
'disgust': 106,
'sadness': 186,
'anticipation': 299,
'surprise': 157,
'anger': 146,
'fear': 283,
'joy': 196}
import matplotlib.pyplot as plt
# Separate keys (emotions) and values (counts)
emotions = list(emotions_dict.keys())
counts = list(emotions_dict.values())
plt.figure(figsize=(12, 6))
# Create a bar char
fig, ax = plt.subplots()
plt.bar(emotions, counts)
# Set chart title and labels for x and y axes
plt.title('Emotion Counts')
plt.xlabel('Emotions')
plt.ylabel('Counts')
ax.set_xticklabels(ax.get_xticklabels(), rotation=90)
# Display the chart
plt.show()
<ipython-input-99-59325c94a792>:17: UserWarning: FixedFormatter should only be used together with FixedLocator
<Figure size 1200x600 with 0 Axes>
From the bar chart above, positive is the most frequent emotion in the data. This does not make sense in the context of the chosen tag. For example, tweet In light of the new lockdown introduced by Boris Johnson, Nicola Sturgeon has released the following statement..."Gonna stay in yer ain bit fur the noo".
The result got from NRCLex is positive and trust, however, this user is ironying UK's lockdown policy, which is negative emotion.
The reason is NRCLex could only perform sentiment analysis based on words and analyzes the literal meaning of words. Irony is a type of figurative language where the intended meaning is opposite to the literal meaning of the words used. This means that the words used in an ironic statement may not necessarily reflect the true emotions of the speaker or writer.
For each user, determine which fraction of a user’s total identified words are in each emotional affect (i.e., normalize the counts for each user). Identify the users with the highest fraction of words in each emotion. Note: You may wish to set a minimum number of tweets for user’s to be considered.
user_twt_dict = {}
for index, row in ukTag_filtered.iterrows():
user = row['user']
tweet = row['tweet']
if user in user_twt_dict:
user_twt_dict[user].append(tweet)
else:
user_twt_dict[user] = [tweet]
highest_frac_dict = {}
for user in user_twt_dict:
if len(user_twt_dict[user]) >= 2:
user_total_dict = {}
for tweet in user_twt_dict[user]:
text_object = NRCLex(tweet)
num_words = text_object.raw_emotion_scores
if user_total_dict == {}:
user_total_dict = num_words
else:
for i in num_words:
if i in user_total_dict:
user_total_dict[i] += num_words[i]
else:
user_total_dict[i] = num_words[i]
if user_total_dict != {}:
max_emotion = max(user_total_dict, key=lambda k: user_total_dict[k])
highest_frac_dict[user] = [max_emotion, user_total_dict[max_emotion]/emotions_dict[max_emotion]]
highest_frac_dict
{'mohith__rai': ['surprise', 0.006369426751592357],
'kingban50512521': ['fear', 0.0035335689045936395],
'loveisbrilliant': ['anticipation', 0.03678929765886288],
'toucheadrian': ['anger', 0.00684931506849315],
'dawnneesom': ['fear', 0.007067137809187279],
'careisthekey': ['anger', 0.00684931506849315],
'politicsaired': ['negative', 0.012958963282937365],
'photopro28': ['trust', 0.022922636103151862],
'nicktubechannel': ['negative', 0.0021598272138228943],
'lindquist_lord': ['fear', 0.01060070671378092],
'hughjam75402652': ['negative', 0.008639308855291577],
'effiedeans': ['positive', 0.0058823529411764705],
'ninsrai': ['positive', 0.0029411764705882353],
'ncknwmn': ['positive', 0.0014705882352941176],
'dsmitheconomics': ['positive', 0.0014705882352941176],
'coronaupdatebot': ['fear', 0.0035335689045936395],
'tangerine94': ['trust', 0.0028653295128939827],
'mythomaniacsuk': ['positive', 0.0029411764705882353],
'supersysez': ['fear', 0.028268551236749116],
'tracywooduk': ['positive', 0.008823529411764706],
'ksutholt': ['negative', 0.0064794816414686825],
'julianstorey': ['trust', 0.017191977077363897],
'romycerratti': ['positive', 0.004411764705882353],
'andy_t_': ['positive', 0.0029411764705882353],
'rupinder_hardy': ['anticipation', 0.013377926421404682],
'ottersun1': ['positive', 0.007352941176470588],
'serotiny23': ['anticipation', 0.0033444816053511705],
'secretgrassroo3': ['positive', 0.010294117647058823],
'wedeservetolive': ['positive', 0.007352941176470588],
'petecityprice': ['positive', 0.0058823529411764705],
'pubrooms': ['positive', 0.0058823529411764705],
'nixonkeri': ['positive', 0.0014705882352941176],
'oxfordlordmayor': ['positive', 0.004411764705882353],
'oxfordstadium': ['positive', 0.004411764705882353],
'ashariqbalkhan': ['trust', 0.0028653295128939827],
'cardstreebeec': ['positive', 0.0029411764705882353],
'sodanamazing': ['positive', 0.0058823529411764705],
'5athiya': ['negative', 0.0021598272138228943]}
max_user_emo = {}
for user in highest_frac_dict:
emotion = highest_frac_dict[user][0]
emotion_score = highest_frac_dict[user][1]
if emotion not in max_user_emo:
max_user_emo[emotion] = (user, emotion_score)
elif emotion_score > max_user_emo[emotion][1]:
max_user_emo[emotion] = (user, emotion_score)
max_user_emo
{'surprise': ('mohith__rai', 0.006369426751592357),
'fear': ('supersysez', 0.028268551236749116),
'anticipation': ('loveisbrilliant', 0.03678929765886288),
'anger': ('toucheadrian', 0.00684931506849315),
'negative': ('politicsaired', 0.012958963282937365),
'trust': ('photopro28', 0.022922636103151862),
'positive': ('secretgrassroo3', 0.010294117647058823)}
Construct a plot(s) to answer the following: Is there a correlation between any dimension of emotion and one of the centrality measures you evaluated? If you see a trend, please provide a hypothesis for why; if you do not see a trend, provide a hypothesis for why centrality of a user has no observed correlation with emotion in your particular dataset.
#any dimension
emo_data = {}
for user in page_centr:
if user in highest_frac_dict:
emo_data[user] = (page_centr[user], highest_frac_dict[user][1])
# Extract x and y coordinates from the dictionary
x_values = [value[0] for value in emo_data.values()]
y_values = [value[1] for value in emo_data.values()]
# Create the scatter plot
plt.scatter(x_values, y_values)
# Add labels and title
plt.xlabel('centrality measure(PageRANK)')
plt.ylabel('All emotions')
plt.title('All emotions VS entrality measure(PageRANK)')
plt.figure(figsize=(19, 6))
# Show the plot
plt.show()
<Figure size 1900x600 with 0 Axes>
#negative
negative_emo = {}
highest_fraction_dict_negative = {}
for user in highest_frac_dict:
if highest_frac_dict[user][0] == 'negative':
highest_fraction_dict_negative[user] = (highest_frac_dict[user][0],highest_frac_dict[user][1])
for user in page_centr:
if user in highest_fraction_dict_negative:
negative_emo[user] = (page_centr[user], highest_fraction_dict_negative[user][1])
x = [value[0] for value in negative_emo.values()]
y = [value[1] for value in negative_emo.values()]
plt.scatter(x, y)
plt.xlabel('centrality measure')
plt.ylabel('negative emotion')
plt.title('Negative emotion VS Pagerank Centrality')
plt.figure(figsize=(19, 6))
plt.show()
<Figure size 1900x600 with 0 Axes>
#positive
positive_emo = {}
highest_fraction_dict_postive = {}
for user in highest_frac_dict:
if highest_frac_dict[user][0] == 'positive':
highest_fraction_dict_postive[user] = (highest_frac_dict[user][0],highest_frac_dict[user][1])
for user in page_centr:
if user in highest_fraction_dict_postive:
positive_emo[user] = (page_centr[user], highest_fraction_dict_postive[user][1])
x = [value[0] for value in positive_emo.values()]
y = [value[1] for value in positive_emo.values()]
plt.scatter(x, y)
plt.xlabel('centrality measure')
plt.ylabel('positive emotion')
plt.title('Positive emotion VS Pagerank Centrality')
plt.figure(figsize=(19, 6))
plt.show()
<Figure size 1900x600 with 0 Axes>
There is no significant correlation between pagerank centrality and positive (or negative) emotion. This maybe due to the number of nodes and the number of edges for chosen tag is close, with #nodes = 1580
and #edges = 1400 and our dataset is small.The influence of any one user is spread out over many other users and there is no strong correlation between pagerank centrality and emotion.